#!/usr/bin/env python
#
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A deep MNIST classifier using convolutional layers.

See extensive documentation at
https://www.tensorflow.org/get_started/mnist/pros
"""
# Disable linter warnings to maintain consistency with tutorial.
# pylint: disable=invalid-name
# pylint: disable=g-bad-import-order

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys
import tempfile
import time

import ray
from ray import tune
from ray.tune import grid_search, register_trainable

from tensorflow.examples.tutorials.mnist import input_data
import numpy as np

import tensorflow as tf

FLAGS = None
status_reporter = None  # used to report training status back to Ray
activation_fn = tf.nn.relu  # e.g. tf.nn.relu


def deepnn(x):
    """deepnn builds the graph for a deep net for classifying digits.

    Args:
        x: an input tensor with the dimensions (N_examples, 784), where 784 is
        the number of pixels in a standard MNIST image.

    Returns:
        A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with
        values equal to the logits of classifying the digit into one of 10
        classes (the digits 0-9). keep_prob is a scalar placeholder for the
        probability of dropout.
    """
    # Reshape to use within a convolutional neural net.
    # Last dimension is for "features" - there is only one here, since images
    # are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
    with tf.name_scope("reshape"):
        x_image = tf.reshape(x, [-1, 28, 28, 1])

    # First convolutional layer - maps one grayscale image to 32 feature maps.
    with tf.name_scope("conv1"):
        W_conv1 = weight_variable([5, 5, 1, 32])
        b_conv1 = bias_variable([32])
        h_conv1 = activation_fn(conv2d(x_image, W_conv1) + b_conv1)

    # Pooling layer - downsamples by 2X.
    with tf.name_scope("pool1"):
        h_pool1 = max_pool_2x2(h_conv1)

    # Second convolutional layer -- maps 32 feature maps to 64.
    with tf.name_scope("conv2"):
        W_conv2 = weight_variable([5, 5, 32, 64])
        b_conv2 = bias_variable([64])
        h_conv2 = activation_fn(conv2d(h_pool1, W_conv2) + b_conv2)

    # Second pooling layer.
    with tf.name_scope("pool2"):
        h_pool2 = max_pool_2x2(h_conv2)

    # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
    # is down to 7x7x64 feature maps -- maps this to 1024 features.
    with tf.name_scope("fc1"):
        W_fc1 = weight_variable([7 * 7 * 64, 1024])
        b_fc1 = bias_variable([1024])

        h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
        h_fc1 = activation_fn(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    # Dropout - controls the complexity of the model, prevents co-adaptation of
    # features.
    with tf.name_scope("dropout"):
        keep_prob = tf.placeholder(tf.float32)
        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    # Map the 1024 features to 10 classes, one for each digit
    with tf.name_scope("fc2"):
        W_fc2 = weight_variable([1024, 10])
        b_fc2 = bias_variable([10])

        y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
    return y_conv, keep_prob


def conv2d(x, W):
    """conv2d returns a 2d convolution layer with full stride."""
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")


def max_pool_2x2(x):
    """max_pool_2x2 downsamples a feature map by 2X."""
    return tf.nn.max_pool(
        x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")


def weight_variable(shape):
    """weight_variable generates a weight variable of a given shape."""
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


def bias_variable(shape):
    """bias_variable generates a bias variable of a given shape."""
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


def main(_):
    # Import data
    for _ in range(10):
        try:
            mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
            break
        except Exception:
            time.sleep(5)

    # Create the model
    x = tf.placeholder(tf.float32, [None, 784])

    # Define loss and optimizer
    y_ = tf.placeholder(tf.float32, [None, 10])

    # Build the graph for the deep net
    y_conv, keep_prob = deepnn(x)

    with tf.name_scope("loss"):
        cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
            labels=y_, logits=y_conv)
    cross_entropy = tf.reduce_mean(cross_entropy)

    with tf.name_scope("adam_optimizer"):
        train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

    with tf.name_scope("accuracy"):
        correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
        correct_prediction = tf.cast(correct_prediction, tf.float32)
    accuracy = tf.reduce_mean(correct_prediction)

    graph_location = tempfile.mkdtemp()
    print("Saving graph to: %s" % graph_location)
    train_writer = tf.summary.FileWriter(graph_location)
    train_writer.add_graph(tf.get_default_graph())

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for i in range(20000):
            batch = mnist.train.next_batch(50)
            if i % 10 == 0:
                train_accuracy = accuracy.eval(feed_dict={
                    x: batch[0],
                    y_: batch[1],
                    keep_prob: 1.0
                })

                # !!! Report status to ray.tune !!!
                if status_reporter:
                    status_reporter(
                        timesteps_total=i, mean_accuracy=train_accuracy)

                print("step %d, training accuracy %g" % (i, train_accuracy))
            train_step.run(feed_dict={
                x: batch[0],
                y_: batch[1],
                keep_prob: 0.5
            })

        print("test accuracy %g" % accuracy.eval(feed_dict={
            x: mnist.test.images,
            y_: mnist.test.labels,
            keep_prob: 1.0
        }))


# !!! Entrypoint for ray.tune !!!
def train(config={"activation": "relu"}, reporter=None):
    global FLAGS, status_reporter, activation_fn
    status_reporter = reporter
    activation_fn = getattr(tf.nn, config["activation"])
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--data_dir",
        type=str,
        default="/tmp/tensorflow/mnist/input_data",
        help="Directory for storing input data")
    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)


# !!! Example of using the ray.tune Python API !!!
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--smoke-test", action="store_true", help="Finish quickly for testing")
    args, _ = parser.parse_known_args()

    register_trainable("train_mnist", train)
    mnist_spec = {
        "stop": {
            "mean_accuracy": 0.99,
            "time_total_s": 600,
        },
        "config": {
            "activation": grid_search(["relu", "elu", "tanh"]),
            # You can pass any serializable object as well
            "foo": grid_search([np.array([1, 2]),
                                np.array([2, 3])]),
        },
    }

    if args.smoke_test:
        mnist_spec["stop"]["training_iteration"] = 2

    ray.init()
    tune.run("train_mnist", name="tune_mnist_test", **mnist_spec)
